Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations160609
Missing cells1202395
Missing cells (%)25.8%
Duplicate rows1003
Duplicate rows (%)0.6%
Total size in memory165.8 MiB
Average record size in memory1.1 KiB

Variable types

DateTime4
Categorical12
Numeric9
Text4

Alerts

year has constant value "2024" Constant
Cancelled Rides by Customer has constant value "1.0" Constant
Cancelled Rides by Driver has constant value "1.0" Constant
Incomplete Rides has constant value "1.0" Constant
Dataset has 1003 (0.6%) duplicate rowsDuplicates
Booking Status is highly overall correlated with Customer Rating and 4 other fieldsHigh correlation
Customer Rating is highly overall correlated with Booking StatusHigh correlation
Driver Cancellation Reason is highly overall correlated with Booking StatusHigh correlation
Driver Ratings is highly overall correlated with Booking StatusHigh correlation
Incomplete Rides Reason is highly overall correlated with Booking StatusHigh correlation
Reason for cancelling by Customer is highly overall correlated with Booking StatusHigh correlation
Ride Distance has 51280 (31.9%) missing values Missing
Booking Value has 51280 (31.9%) missing values Missing
Avg VTAT has 11207 (7.0%) missing values Missing
Avg CTAT has 51280 (31.9%) missing values Missing
Cancelled Rides by Customer has 149355 (93.0%) missing values Missing
Reason for cancelling by Customer has 149355 (93.0%) missing values Missing
Cancelled Rides by Driver has 131790 (82.1%) missing values Missing
Driver Cancellation Reason has 131790 (82.1%) missing values Missing
Incomplete Rides has 150946 (94.0%) missing values Missing
Incomplete Rides Reason has 150946 (94.0%) missing values Missing
Driver Ratings has 60943 (37.9%) missing values Missing
Customer Rating has 60943 (37.9%) missing values Missing
Payment Method has 51280 (31.9%) missing values Missing

Reproduction

Analysis started2026-01-29 05:50:44.531106
Analysis finished2026-01-29 05:51:35.135569
Duration50.6 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct149532
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Minimum2024-01-01 00:19:34
Maximum2024-12-30 23:36:11
Invalid dates0
Invalid dates (%)0.0%
2026-01-29T11:21:35.287511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:35.443934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Date
Date

Distinct365
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Minimum2024-01-01 00:00:00
Maximum2024-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-29T11:21:35.573673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:35.755685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Date

Distinct62910
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Minimum2026-01-29 00:00:00
Maximum2026-01-29 23:59:59
Invalid dates0
Invalid dates (%)0.0%
2026-01-29T11:21:35.985270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:36.186076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
2024
160609 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters642436
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 160609
100.0%

Length

2026-01-29T11:21:36.347748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:36.432307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2024 160609
100.0%

Most occurring characters

ValueCountFrequency (%)
2 321218
50.0%
0 160609
25.0%
4 160609
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 642436
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 321218
50.0%
0 160609
25.0%
4 160609
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 642436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 321218
50.0%
0 160609
25.0%
4 160609
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 642436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 321218
50.0%
0 160609
25.0%
4 160609
25.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.548232
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-29T11:21:36.507632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3693271
Coefficient of variation (CV)0.51453996
Kurtosis-1.1423814
Mean6.548232
Median Absolute Deviation (MAD)3
Skewness-0.040028967
Sum1051705
Variance11.352365
MonotonicityNot monotonic
2026-01-29T11:21:36.589629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 19323
12.0%
10 14861
9.3%
8 13102
8.2%
4 13092
8.2%
1 12933
8.1%
5 12844
8.0%
3 12791
8.0%
6 12540
7.8%
11 12450
7.8%
12 12332
7.7%
Other values (2) 24341
15.2%
ValueCountFrequency (%)
1 12933
8.1%
2 12017
7.5%
3 12791
8.0%
4 13092
8.2%
5 12844
8.0%
6 12540
7.8%
7 19323
12.0%
8 13102
8.2%
9 12324
7.7%
10 14861
9.3%
ValueCountFrequency (%)
12 12332
7.7%
11 12450
7.8%
10 14861
9.3%
9 12324
7.7%
8 13102
8.2%
7 19323
12.0%
6 12540
7.8%
5 12844
8.0%
4 13092
8.2%
3 12791
8.0%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.175955
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-29T11:21:36.704101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7790546
Coefficient of variation (CV)0.57848448
Kurtosis-1.1882037
Mean15.175955
Median Absolute Deviation (MAD)8
Skewness0.090717827
Sum2437395
Variance77.071799
MonotonicityNot monotonic
2026-01-29T11:21:36.807033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 5922
 
3.7%
4 5908
 
3.7%
3 5870
 
3.7%
12 5842
 
3.6%
2 5770
 
3.6%
10 5754
 
3.6%
11 5736
 
3.6%
15 5725
 
3.6%
6 5500
 
3.4%
1 5491
 
3.4%
Other values (21) 103091
64.2%
ValueCountFrequency (%)
1 5491
3.4%
2 5770
3.6%
3 5870
3.7%
4 5908
3.7%
5 5922
3.7%
6 5500
3.4%
7 5343
3.3%
8 5382
3.4%
9 5481
3.4%
10 5754
3.6%
ValueCountFrequency (%)
31 2405
1.5%
30 4466
2.8%
29 4890
3.0%
28 4864
3.0%
27 4901
3.1%
26 5035
3.1%
25 4927
3.1%
24 4981
3.1%
23 4866
3.0%
22 5012
3.1%

weekday
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
Saturday
23413 
Thursday
23411 
Wednesday
23264 
Monday
23026 
Friday
22780 
Other values (2)
44715 

Length

Max length9
Median length8
Mean length7.1568218
Min length6

Characters and Unicode

Total characters1149450
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowFriday
3rd rowFriday
4th rowMonday
5th rowMonday

Common Values

ValueCountFrequency (%)
Saturday 23413
14.6%
Thursday 23411
14.6%
Wednesday 23264
14.5%
Monday 23026
14.3%
Friday 22780
14.2%
Sunday 22359
13.9%
Tuesday 22356
13.9%

Length

2026-01-29T11:21:36.921988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:37.019895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
saturday 23413
14.6%
thursday 23411
14.6%
wednesday 23264
14.5%
monday 23026
14.3%
friday 22780
14.2%
sunday 22359
13.9%
tuesday 22356
13.9%

Most occurring characters

ValueCountFrequency (%)
a 184022
16.0%
d 183873
16.0%
y 160609
14.0%
u 91539
8.0%
r 69604
 
6.1%
s 69031
 
6.0%
e 68884
 
6.0%
n 68649
 
6.0%
S 45772
 
4.0%
T 45767
 
4.0%
Other values (7) 161700
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 988841
86.0%
Uppercase Letter 160609
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 184022
18.6%
d 183873
18.6%
y 160609
16.2%
u 91539
9.3%
r 69604
 
7.0%
s 69031
 
7.0%
e 68884
 
7.0%
n 68649
 
6.9%
t 23413
 
2.4%
h 23411
 
2.4%
Other values (2) 45806
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S 45772
28.5%
T 45767
28.5%
W 23264
14.5%
M 23026
14.3%
F 22780
14.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1149450
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 184022
16.0%
d 183873
16.0%
y 160609
14.0%
u 91539
8.0%
r 69604
 
6.1%
s 69031
 
6.0%
e 68884
 
6.0%
n 68649
 
6.0%
S 45772
 
4.0%
T 45767
 
4.0%
Other values (7) 161700
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1149450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 184022
16.0%
d 183873
16.0%
y 160609
14.0%
u 91539
8.0%
r 69604
 
6.1%
s 69031
 
6.0%
e 68884
 
6.0%
n 68649
 
6.0%
S 45772
 
4.0%
T 45767
 
4.0%
Other values (7) 161700
14.1%

hour
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.032302
Minimum0
Maximum23
Zeros1469
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-29T11:21:37.139045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median15
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.4145955
Coefficient of variation (CV)0.38586652
Kurtosis-0.65176385
Mean14.032302
Median Absolute Deviation (MAD)4
Skewness-0.41504767
Sum2253714
Variance29.317845
MonotonicityNot monotonic
2026-01-29T11:21:37.267121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18 13321
 
8.3%
17 11859
 
7.4%
19 11828
 
7.4%
20 10313
 
6.4%
10 10292
 
6.4%
16 10281
 
6.4%
11 8996
 
5.6%
9 8828
 
5.5%
15 8769
 
5.5%
21 8651
 
5.4%
Other values (14) 57471
35.8%
ValueCountFrequency (%)
0 1469
 
0.9%
1 1456
 
0.9%
2 1430
 
0.9%
3 1472
 
0.9%
4 1412
 
0.9%
5 2960
 
1.8%
6 4486
2.8%
7 5817
3.6%
8 7357
4.6%
9 8828
5.5%
ValueCountFrequency (%)
23 2954
 
1.8%
22 5793
3.6%
21 8651
5.4%
20 10313
6.4%
19 11828
7.4%
18 13321
8.3%
17 11859
7.4%
16 10281
6.4%
15 8769
5.5%
14 7523
4.7%
Distinct365
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Minimum2024-01-01 00:00:00
Maximum2024-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-29T11:21:37.385309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:37.551677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Event
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
Normal Day
73084 
Wedding_Season
33173 
Monsoon
31838 
Navratri
 
6673
Monsoon_Sales
 
6318
Other values (11)
9523 

Length

Max length16
Median length14
Mean length10.255036
Min length4

Characters and Unicode

Total characters1647051
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal Day
2nd rowWedding_Season
3rd rowMonsoon
4th rowNormal Day
5th rowNormal Day

Common Values

ValueCountFrequency (%)
Normal Day 73084
45.5%
Wedding_Season 33173
20.7%
Monsoon 31838
19.8%
Navratri 6673
 
4.2%
Monsoon_Sales 6318
 
3.9%
Diwali 2466
 
1.5%
Festive_Sales 2058
 
1.3%
Eid-ul-Adha 834
 
0.5%
Eid-ul-Fitr 833
 
0.5%
Christmas 796
 
0.5%
Other values (6) 2536
 
1.6%

Length

2026-01-29T11:21:37.693088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
day 73940
31.4%
normal 73084
31.0%
wedding_season 33173
14.1%
monsoon 31838
13.5%
navratri 6673
 
2.8%
monsoon_sales 6318
 
2.7%
diwali 2466
 
1.0%
festive_sales 2058
 
0.9%
eid-ul-adha 834
 
0.4%
eid-ul-fitr 833
 
0.4%
Other values (9) 4173
 
1.8%

Most occurring characters

ValueCountFrequency (%)
o 221106
13.4%
a 208164
12.6%
n 144720
 
8.8%
r 88933
 
5.4%
l 86426
 
5.2%
s 84271
 
5.1%
e 82196
 
5.0%
N 80173
 
4.9%
D 76864
 
4.7%
74781
 
4.5%
Other values (28) 499417
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1248781
75.8%
Uppercase Letter 278606
 
16.9%
Space Separator 74781
 
4.5%
Connector Punctuation 41549
 
2.5%
Dash Punctuation 3334
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 221106
17.7%
a 208164
16.7%
n 144720
11.6%
r 88933
7.1%
l 86426
 
6.9%
s 84271
 
6.7%
e 82196
 
6.6%
y 74365
 
6.0%
m 73880
 
5.9%
d 70080
 
5.6%
Other values (10) 114640
9.2%
Uppercase Letter
ValueCountFrequency (%)
N 80173
28.8%
D 76864
27.6%
S 41549
14.9%
M 38156
13.7%
W 33173
11.9%
F 2891
 
1.0%
E 1667
 
0.6%
A 834
 
0.3%
C 796
 
0.3%
R 452
 
0.2%
Other values (5) 2051
 
0.7%
Space Separator
ValueCountFrequency (%)
74781
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 41549
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3334
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1527387
92.7%
Common 119664
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 221106
14.5%
a 208164
13.6%
n 144720
 
9.5%
r 88933
 
5.8%
l 86426
 
5.7%
s 84271
 
5.5%
e 82196
 
5.4%
N 80173
 
5.2%
D 76864
 
5.0%
y 74365
 
4.9%
Other values (25) 380169
24.9%
Common
ValueCountFrequency (%)
74781
62.5%
_ 41549
34.7%
- 3334
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1647051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 221106
13.4%
a 208164
12.6%
n 144720
 
8.8%
r 88933
 
5.4%
l 86426
 
5.2%
s 84271
 
5.1%
e 82196
 
5.0%
N 80173
 
4.9%
D 76864
 
4.7%
74781
 
4.5%
Other values (28) 499417
30.3%
Distinct148767
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
2026-01-29T11:21:38.018405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1927308
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137241 ?
Unique (%)85.5%

Sample

1st row"CNR5884300"
2nd row"CNR1326809"
3rd row"CNR8494506"
4th row"CNR8906825"
5th row"CNR1950162"
ValueCountFrequency (%)
cnr8281555 4
 
< 0.1%
cnr2918341 4
 
< 0.1%
cnr2624320 4
 
< 0.1%
cnr2080548 4
 
< 0.1%
cnr4767660 4
 
< 0.1%
cnr1015148 4
 
< 0.1%
cnr9295320 4
 
< 0.1%
cnr8928908 4
 
< 0.1%
cnr2740651 4
 
< 0.1%
cnr5229368 4
 
< 0.1%
Other values (148757) 160569
> 99.9%
2026-01-29T11:21:38.409331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 321218
16.7%
C 160609
 
8.3%
N 160609
 
8.3%
R 160609
 
8.3%
9 114665
 
5.9%
7 114387
 
5.9%
4 114378
 
5.9%
6 114321
 
5.9%
5 114258
 
5.9%
2 114190
 
5.9%
Other values (4) 438064
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124263
58.3%
Uppercase Letter 481827
25.0%
Other Punctuation 321218
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 114665
10.2%
7 114387
10.2%
4 114378
10.2%
6 114321
10.2%
5 114258
10.2%
2 114190
10.2%
1 114164
10.2%
8 114048
10.1%
3 113798
10.1%
0 96054
8.5%
Uppercase Letter
ValueCountFrequency (%)
C 160609
33.3%
N 160609
33.3%
R 160609
33.3%
Other Punctuation
ValueCountFrequency (%)
" 321218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1445481
75.0%
Latin 481827
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
" 321218
22.2%
9 114665
 
7.9%
7 114387
 
7.9%
4 114378
 
7.9%
6 114321
 
7.9%
5 114258
 
7.9%
2 114190
 
7.9%
1 114164
 
7.9%
8 114048
 
7.9%
3 113798
 
7.9%
Latin
ValueCountFrequency (%)
C 160609
33.3%
N 160609
33.3%
R 160609
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1927308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 321218
16.7%
C 160609
 
8.3%
N 160609
 
8.3%
R 160609
 
8.3%
9 114665
 
5.9%
7 114387
 
5.9%
4 114378
 
5.9%
6 114321
 
5.9%
5 114258
 
5.9%
2 114190
 
5.9%
Other values (4) 438064
22.7%

Booking Status
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
Completed
99666 
Cancelled by Driver
28819 
Cancelled by Customer
11254 
No Driver Found
11207 
Incomplete
 
9663

Length

Max length21
Median length9
Mean length12.114041
Min length9

Characters and Unicode

Total characters1945624
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Driver Found
2nd rowIncomplete
3rd rowCompleted
4th rowCompleted
5th rowCompleted

Common Values

ValueCountFrequency (%)
Completed 99666
62.1%
Cancelled by Driver 28819
 
17.9%
Cancelled by Customer 11254
 
7.0%
No Driver Found 11207
 
7.0%
Incomplete 9663
 
6.0%

Length

2026-01-29T11:21:38.529421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:38.598155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
completed 99666
37.9%
cancelled 40073
15.2%
by 40073
15.2%
driver 40026
15.2%
customer 11254
 
4.3%
no 11207
 
4.3%
found 11207
 
4.3%
incomplete 9663
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e 350084
18.0%
l 189475
9.7%
C 150993
 
7.8%
d 150946
 
7.8%
o 142997
 
7.3%
m 120583
 
6.2%
t 120583
 
6.2%
p 109329
 
5.6%
102560
 
5.3%
r 91306
 
4.7%
Other values (13) 416768
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1619968
83.3%
Uppercase Letter 223096
 
11.5%
Space Separator 102560
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 350084
21.6%
l 189475
11.7%
d 150946
9.3%
o 142997
8.8%
m 120583
 
7.4%
t 120583
 
7.4%
p 109329
 
6.7%
r 91306
 
5.6%
n 60943
 
3.8%
c 49736
 
3.1%
Other values (7) 233986
14.4%
Uppercase Letter
ValueCountFrequency (%)
C 150993
67.7%
D 40026
 
17.9%
N 11207
 
5.0%
F 11207
 
5.0%
I 9663
 
4.3%
Space Separator
ValueCountFrequency (%)
102560
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1843064
94.7%
Common 102560
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 350084
19.0%
l 189475
10.3%
C 150993
8.2%
d 150946
8.2%
o 142997
7.8%
m 120583
 
6.5%
t 120583
 
6.5%
p 109329
 
5.9%
r 91306
 
5.0%
n 60943
 
3.3%
Other values (12) 355825
19.3%
Common
ValueCountFrequency (%)
102560
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1945624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 350084
18.0%
l 189475
9.7%
C 150993
 
7.8%
d 150946
 
7.8%
o 142997
 
7.3%
m 120583
 
6.2%
t 120583
 
6.2%
p 109329
 
5.6%
102560
 
5.3%
r 91306
 
4.7%
Other values (13) 416768
21.4%
Distinct148788
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
2026-01-29T11:21:38.861725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1927308
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137264 ?
Unique (%)85.5%

Sample

1st row"CID1982111"
2nd row"CID4604802"
3rd row"CID9202816"
4th row"CID2610914"
5th row"CID9933542"
ValueCountFrequency (%)
cid8418985 4
 
< 0.1%
cid9681675 4
 
< 0.1%
cid1044355 4
 
< 0.1%
cid4833362 4
 
< 0.1%
cid5102441 4
 
< 0.1%
cid8253544 4
 
< 0.1%
cid4701042 4
 
< 0.1%
cid8165460 4
 
< 0.1%
cid7303248 4
 
< 0.1%
cid9629712 4
 
< 0.1%
Other values (148778) 160569
> 99.9%
2026-01-29T11:21:39.324059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 321218
16.7%
C 160609
 
8.3%
I 160609
 
8.3%
D 160609
 
8.3%
4 114765
 
6.0%
6 114587
 
5.9%
1 114561
 
5.9%
9 114307
 
5.9%
5 114020
 
5.9%
7 114013
 
5.9%
Other values (4) 438010
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1124263
58.3%
Uppercase Letter 481827
25.0%
Other Punctuation 321218
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 114765
10.2%
6 114587
10.2%
1 114561
10.2%
9 114307
10.2%
5 114020
10.1%
7 114013
10.1%
3 113997
10.1%
8 113986
10.1%
2 113589
10.1%
0 96438
8.6%
Uppercase Letter
ValueCountFrequency (%)
C 160609
33.3%
I 160609
33.3%
D 160609
33.3%
Other Punctuation
ValueCountFrequency (%)
" 321218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1445481
75.0%
Latin 481827
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
" 321218
22.2%
4 114765
 
7.9%
6 114587
 
7.9%
1 114561
 
7.9%
9 114307
 
7.9%
5 114020
 
7.9%
7 114013
 
7.9%
3 113997
 
7.9%
8 113986
 
7.9%
2 113589
 
7.9%
Latin
ValueCountFrequency (%)
C 160609
33.3%
I 160609
33.3%
D 160609
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1927308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 321218
16.7%
C 160609
 
8.3%
I 160609
 
8.3%
D 160609
 
8.3%
4 114765
 
6.0%
6 114587
 
5.9%
1 114561
 
5.9%
9 114307
 
5.9%
5 114020
 
5.9%
7 114013
 
5.9%
Other values (4) 438010
22.7%

Vehicle Type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
Auto
40066 
Go Mini
31873 
Go Sedan
29042 
Bike
24108 
Premier Sedan
19453 
Other values (2)
16067 

Length

Max length13
Median length8
Mean length6.5683181
Min length4

Characters and Unicode

Total characters1054931
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweBike
2nd rowGo Sedan
3rd rowAuto
4th rowPremier Sedan
5th rowBike

Common Values

ValueCountFrequency (%)
Auto 40066
24.9%
Go Mini 31873
19.8%
Go Sedan 29042
18.1%
Bike 24108
15.0%
Premier Sedan 19453
12.1%
eBike 11285
 
7.0%
Uber XL 4782
 
3.0%

Length

2026-01-29T11:21:39.441609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:39.519690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
go 60915
24.8%
sedan 48495
19.7%
auto 40066
16.3%
mini 31873
13.0%
bike 24108
 
9.8%
premier 19453
 
7.9%
ebike 11285
 
4.6%
uber 4782
 
1.9%
xl 4782
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 138861
13.2%
i 118592
11.2%
o 100981
 
9.6%
85150
 
8.1%
n 80368
 
7.6%
G 60915
 
5.8%
d 48495
 
4.6%
a 48495
 
4.6%
S 48495
 
4.6%
r 43688
 
4.1%
Other values (12) 280891
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 719240
68.2%
Uppercase Letter 250541
 
23.7%
Space Separator 85150
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 138861
19.3%
i 118592
16.5%
o 100981
14.0%
n 80368
11.2%
d 48495
 
6.7%
a 48495
 
6.7%
r 43688
 
6.1%
u 40066
 
5.6%
t 40066
 
5.6%
k 35393
 
4.9%
Other values (2) 24235
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
G 60915
24.3%
S 48495
19.4%
A 40066
16.0%
B 35393
14.1%
M 31873
12.7%
P 19453
 
7.8%
U 4782
 
1.9%
X 4782
 
1.9%
L 4782
 
1.9%
Space Separator
ValueCountFrequency (%)
85150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 969781
91.9%
Common 85150
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 138861
14.3%
i 118592
12.2%
o 100981
10.4%
n 80368
 
8.3%
G 60915
 
6.3%
d 48495
 
5.0%
a 48495
 
5.0%
S 48495
 
5.0%
r 43688
 
4.5%
u 40066
 
4.1%
Other values (11) 240825
24.8%
Common
ValueCountFrequency (%)
85150
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1054931
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 138861
13.2%
i 118592
11.2%
o 100981
 
9.6%
85150
 
8.1%
n 80368
 
7.6%
G 60915
 
5.8%
d 48495
 
4.6%
a 48495
 
4.6%
S 48495
 
4.6%
r 43688
 
4.1%
Other values (12) 280891
26.6%
Distinct176
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2026-01-29T11:21:39.881321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length19
Mean length11.504978
Min length3

Characters and Unicode

Total characters1847803
Distinct characters58
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPalam Vihar
2nd rowShastri Nagar
3rd rowKhandsa
4th rowCentral Secretariat
5th rowGhitorni Village
ValueCountFrequency (%)
nagar 15520
 
5.2%
vihar 9032
 
3.0%
chowk 7376
 
2.5%
park 5593
 
1.9%
gurgaon 5374
 
1.8%
sector 5354
 
1.8%
road 4546
 
1.5%
noida 4507
 
1.5%
city 3734
 
1.3%
garden 3655
 
1.2%
Other values (214) 233229
78.3%
2026-01-29T11:21:40.271233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 297186
16.1%
r 137624
 
7.4%
137311
 
7.4%
i 117500
 
6.4%
h 95650
 
5.2%
n 80066
 
4.3%
e 77209
 
4.2%
o 73657
 
4.0%
t 60143
 
3.3%
u 57330
 
3.1%
Other values (48) 714127
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1374375
74.4%
Uppercase Letter 323617
 
17.5%
Space Separator 137311
 
7.4%
Decimal Number 12500
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 297186
21.6%
r 137624
10.0%
i 117500
 
8.5%
h 95650
 
7.0%
n 80066
 
5.8%
e 77209
 
5.6%
o 73657
 
5.4%
t 60143
 
4.4%
u 57330
 
4.2%
d 55757
 
4.1%
Other values (15) 322253
23.4%
Uppercase Letter
ValueCountFrequency (%)
S 32801
 
10.1%
N 28251
 
8.7%
P 25659
 
7.9%
M 25637
 
7.9%
G 25292
 
7.8%
C 23798
 
7.4%
K 18420
 
5.7%
B 17417
 
5.4%
R 17327
 
5.4%
I 15594
 
4.8%
Other values (14) 93421
28.9%
Decimal Number
ValueCountFrequency (%)
2 2722
21.8%
1 2699
21.6%
5 1790
14.3%
6 1785
14.3%
3 900
 
7.2%
4 892
 
7.1%
9 870
 
7.0%
8 842
 
6.7%
Space Separator
ValueCountFrequency (%)
137311
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1697992
91.9%
Common 149811
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 297186
17.5%
r 137624
 
8.1%
i 117500
 
6.9%
h 95650
 
5.6%
n 80066
 
4.7%
e 77209
 
4.5%
o 73657
 
4.3%
t 60143
 
3.5%
u 57330
 
3.4%
d 55757
 
3.3%
Other values (39) 645870
38.0%
Common
ValueCountFrequency (%)
137311
91.7%
2 2722
 
1.8%
1 2699
 
1.8%
5 1790
 
1.2%
6 1785
 
1.2%
3 900
 
0.6%
4 892
 
0.6%
9 870
 
0.6%
8 842
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1847803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 297186
16.1%
r 137624
 
7.4%
137311
 
7.4%
i 117500
 
6.4%
h 95650
 
5.2%
n 80066
 
4.3%
e 77209
 
4.2%
o 73657
 
4.0%
t 60143
 
3.3%
u 57330
 
3.1%
Other values (48) 714127
38.6%
Distinct176
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2026-01-29T11:21:40.474855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length20
Mean length11.513022
Min length3

Characters and Unicode

Total characters1849095
Distinct characters58
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJhilmil
2nd rowGurgaon Sector 56
3rd rowMalviya Nagar
4th rowInderlok
5th rowKhan Market
ValueCountFrequency (%)
nagar 15590
 
5.2%
vihar 9153
 
3.1%
chowk 7152
 
2.4%
sector 5495
 
1.8%
gurgaon 5474
 
1.8%
park 5377
 
1.8%
noida 4616
 
1.5%
road 4420
 
1.5%
gate 3729
 
1.2%
delhi 3720
 
1.2%
Other values (214) 233661
78.3%
2026-01-29T11:21:40.834670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 296655
16.0%
137778
 
7.5%
r 137702
 
7.4%
i 117601
 
6.4%
h 96066
 
5.2%
n 80097
 
4.3%
e 77672
 
4.2%
o 74016
 
4.0%
t 60267
 
3.3%
u 57151
 
3.1%
Other values (48) 714090
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1374931
74.4%
Uppercase Letter 323513
 
17.5%
Space Separator 137778
 
7.5%
Decimal Number 12873
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 296655
21.6%
r 137702
10.0%
i 117601
 
8.6%
h 96066
 
7.0%
n 80097
 
5.8%
e 77672
 
5.6%
o 74016
 
5.4%
t 60267
 
4.4%
u 57151
 
4.2%
d 55343
 
4.0%
Other values (15) 322361
23.4%
Uppercase Letter
ValueCountFrequency (%)
S 32779
 
10.1%
N 28630
 
8.8%
P 25583
 
7.9%
M 25563
 
7.9%
G 25538
 
7.9%
C 23306
 
7.2%
K 18424
 
5.7%
B 17183
 
5.3%
R 17166
 
5.3%
V 15457
 
4.8%
Other values (14) 93884
29.0%
Decimal Number
ValueCountFrequency (%)
1 2742
21.3%
2 2696
20.9%
5 1883
14.6%
6 1865
14.5%
3 942
 
7.3%
4 941
 
7.3%
8 916
 
7.1%
9 888
 
6.9%
Space Separator
ValueCountFrequency (%)
137778
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1698444
91.9%
Common 150651
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 296655
17.5%
r 137702
 
8.1%
i 117601
 
6.9%
h 96066
 
5.7%
n 80097
 
4.7%
e 77672
 
4.6%
o 74016
 
4.4%
t 60267
 
3.5%
u 57151
 
3.4%
d 55343
 
3.3%
Other values (39) 645874
38.0%
Common
ValueCountFrequency (%)
137778
91.5%
1 2742
 
1.8%
2 2696
 
1.8%
5 1883
 
1.2%
6 1865
 
1.2%
3 942
 
0.6%
4 941
 
0.6%
8 916
 
0.6%
9 888
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1849095
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 296655
16.0%
137778
 
7.5%
r 137702
 
7.4%
i 117601
 
6.4%
h 96066
 
5.2%
n 80097
 
4.3%
e 77672
 
4.2%
o 74016
 
4.0%
t 60267
 
3.3%
u 57151
 
3.1%
Other values (48) 714090
38.6%

Ride Distance
Real number (ℝ)

Missing 

Distinct4901
Distinct (%)4.5%
Missing51280
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean24.616326
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-29T11:21:41.024422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.97
Q112.43
median23.67
Q336.79
95-th percentile47.35
Maximum50
Range49
Interquartile range (IQR)24.36

Descriptive statistics

Standard deviation13.999738
Coefficient of variation (CV)0.56871762
Kurtosis-1.2118066
Mean24.616326
Median Absolute Deviation (MAD)12.09
Skewness0.13028125
Sum2691278.3
Variance195.99268
MonotonicityNot monotonic
2026-01-29T11:21:41.165906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.61 47
 
< 0.1%
14.93 46
 
< 0.1%
3.44 45
 
< 0.1%
17.31 44
 
< 0.1%
14.47 44
 
< 0.1%
12.52 43
 
< 0.1%
12.87 43
 
< 0.1%
15.02 42
 
< 0.1%
8.69 41
 
< 0.1%
8.97 41
 
< 0.1%
Other values (4891) 108893
67.8%
(Missing) 51280
31.9%
ValueCountFrequency (%)
1 6
< 0.1%
1.01 3
 
< 0.1%
1.02 5
< 0.1%
1.03 5
< 0.1%
1.04 3
 
< 0.1%
1.05 3
 
< 0.1%
1.06 5
< 0.1%
1.07 8
< 0.1%
1.08 1
 
< 0.1%
1.09 6
< 0.1%
ValueCountFrequency (%)
50 10
 
< 0.1%
49.99 20
< 0.1%
49.98 22
< 0.1%
49.97 22
< 0.1%
49.96 21
< 0.1%
49.95 17
< 0.1%
49.94 21
< 0.1%
49.93 25
< 0.1%
49.92 17
< 0.1%
49.91 19
< 0.1%

Booking Value
Real number (ℝ)

Missing 

Distinct2566
Distinct (%)2.3%
Missing51280
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean508.63849
Minimum50
Maximum4277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-29T11:21:41.304652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile91
Q1234
median414
Q3689
95-th percentile1225
Maximum4277
Range4227
Interquartile range (IQR)455

Descriptive statistics

Standard deviation396.31267
Coefficient of variation (CV)0.77916373
Kurtosis9.9549601
Mean508.63849
Median Absolute Deviation (MAD)211
Skewness2.2955538
Sum55608938
Variance157063.73
MonotonicityNot monotonic
2026-01-29T11:21:41.429506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 186
 
0.1%
176 185
 
0.1%
125 182
 
0.1%
157 181
 
0.1%
470 181
 
0.1%
249 180
 
0.1%
408 180
 
0.1%
240 180
 
0.1%
98 179
 
0.1%
186 178
 
0.1%
Other values (2556) 107517
66.9%
(Missing) 51280
31.9%
ValueCountFrequency (%)
50 132
0.1%
51 113
0.1%
52 123
0.1%
53 146
0.1%
54 103
0.1%
55 111
0.1%
56 122
0.1%
57 120
0.1%
58 118
0.1%
59 125
0.1%
ValueCountFrequency (%)
4277 2
< 0.1%
4228 1
< 0.1%
4220 1
< 0.1%
4202 1
< 0.1%
4133 2
< 0.1%
4109 1
< 0.1%
4093 1
< 0.1%
4088 1
< 0.1%
4060 1
< 0.1%
4044 1
< 0.1%

Avg VTAT
Real number (ℝ)

Missing 

Distinct181
Distinct (%)0.1%
Missing11207
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean8.4521171
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-29T11:21:41.549192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.9
Q15.3
median8.3
Q311.3
95-th percentile14.6
Maximum20
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7720348
Coefficient of variation (CV)0.44628283
Kurtosis-0.5949816
Mean8.4521171
Median Absolute Deviation (MAD)3
Skewness0.30841726
Sum1262763.2
Variance14.228246
MonotonicityNot monotonic
2026-01-29T11:21:41.662957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 1352
 
0.8%
5.8 1348
 
0.8%
6.5 1347
 
0.8%
6 1340
 
0.8%
9.5 1339
 
0.8%
6.2 1335
 
0.8%
7.4 1323
 
0.8%
7.7 1320
 
0.8%
8.9 1319
 
0.8%
5.1 1315
 
0.8%
Other values (171) 136064
84.7%
(Missing) 11207
 
7.0%
ValueCountFrequency (%)
2 436
0.3%
2.1 902
0.6%
2.2 830
0.5%
2.3 843
0.5%
2.4 869
0.5%
2.5 888
0.6%
2.6 878
0.5%
2.7 834
0.5%
2.8 873
0.5%
2.9 884
0.6%
ValueCountFrequency (%)
20 39
< 0.1%
19.9 93
0.1%
19.8 83
0.1%
19.7 73
< 0.1%
19.6 79
< 0.1%
19.5 61
< 0.1%
19.4 97
0.1%
19.3 64
< 0.1%
19.2 84
0.1%
19.1 65
< 0.1%

Avg CTAT
Real number (ℝ)

Missing 

Distinct351
Distinct (%)0.3%
Missing51280
Missing (%)31.9%
Infinite0
Infinite (%)0.0%
Mean29.137421
Minimum10
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-29T11:21:41.782387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15.8
Q121.6
median28.7
Q336.8
95-th percentile43.3
Maximum45
Range35
Interquartile range (IQR)15.2

Descriptive statistics

Standard deviation8.9071902
Coefficient of variation (CV)0.3056959
Kurtosis-1.1256407
Mean29.137421
Median Absolute Deviation (MAD)7.6
Skewness0.047091738
Sum3185565.1
Variance79.338037
MonotonicityNot monotonic
2026-01-29T11:21:41.907825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.8 426
 
0.3%
18.5 423
 
0.3%
20 419
 
0.3%
23.5 419
 
0.3%
28.1 417
 
0.3%
21.1 412
 
0.3%
25.9 410
 
0.3%
20.5 409
 
0.3%
22.4 409
 
0.3%
26.6 408
 
0.3%
Other values (341) 105177
65.5%
(Missing) 51280
31.9%
ValueCountFrequency (%)
10 24
< 0.1%
10.1 53
< 0.1%
10.2 49
< 0.1%
10.3 45
< 0.1%
10.4 54
< 0.1%
10.5 49
< 0.1%
10.6 50
< 0.1%
10.7 50
< 0.1%
10.8 50
< 0.1%
10.9 44
< 0.1%
ValueCountFrequency (%)
45 149
0.1%
44.9 336
0.2%
44.8 333
0.2%
44.7 352
0.2%
44.6 332
0.2%
44.5 318
0.2%
44.4 340
0.2%
44.3 282
0.2%
44.2 320
0.2%
44.1 335
0.2%

Cancelled Rides by Customer
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing149355
Missing (%)93.0%
Memory size9.8 MiB
1.0
11254 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33762
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 11254
 
7.0%
(Missing) 149355
93.0%

Length

2026-01-29T11:21:42.014811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:42.061913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 11254
100.0%

Most occurring characters

ValueCountFrequency (%)
1 11254
33.3%
. 11254
33.3%
0 11254
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22508
66.7%
Other Punctuation 11254
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11254
50.0%
0 11254
50.0%
Other Punctuation
ValueCountFrequency (%)
. 11254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33762
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11254
33.3%
. 11254
33.3%
0 11254
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11254
33.3%
. 11254
33.3%
0 11254
33.3%

Reason for cancelling by Customer
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing149355
Missing (%)93.0%
Memory size10.0 MiB
Change of plans
2531 
Wrong Address
2515 
Driver is not moving towards pickup location
2512 
Driver asked to cancel
2452 
AC is not working
1244 

Length

Max length44
Median length17
Mean length22.772348
Min length13

Characters and Unicode

Total characters256280
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDriver is not moving towards pickup location
2nd rowDriver is not moving towards pickup location
3rd rowDriver asked to cancel
4th rowDriver is not moving towards pickup location
5th rowDriver asked to cancel

Common Values

ValueCountFrequency (%)
Change of plans 2531
 
1.6%
Wrong Address 2515
 
1.6%
Driver is not moving towards pickup location 2512
 
1.6%
Driver asked to cancel 2452
 
1.5%
AC is not working 1244
 
0.8%
(Missing) 149355
93.0%

Length

2026-01-29T11:21:42.125295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:42.199322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
driver 4964
 
11.0%
is 3756
 
8.3%
not 3756
 
8.3%
change 2531
 
5.6%
plans 2531
 
5.6%
of 2531
 
5.6%
wrong 2515
 
5.6%
address 2515
 
5.6%
pickup 2512
 
5.6%
location 2512
 
5.6%
Other values (7) 14868
33.0%

Most occurring characters

ValueCountFrequency (%)
33737
13.2%
o 22546
 
8.8%
n 20053
 
7.8%
r 18714
 
7.3%
i 17500
 
6.8%
s 16281
 
6.4%
a 14990
 
5.8%
e 14914
 
5.8%
t 11232
 
4.4%
d 9994
 
3.9%
Other values (15) 76319
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 207530
81.0%
Space Separator 33737
 
13.2%
Uppercase Letter 15013
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 22546
10.9%
n 20053
 
9.7%
r 18714
 
9.0%
i 17500
 
8.4%
s 16281
 
7.8%
a 14990
 
7.2%
e 14914
 
7.2%
t 11232
 
5.4%
d 9994
 
4.8%
c 9928
 
4.8%
Other values (10) 51378
24.8%
Uppercase Letter
ValueCountFrequency (%)
D 4964
33.1%
C 3775
25.1%
A 3759
25.0%
W 2515
16.8%
Space Separator
ValueCountFrequency (%)
33737
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 222543
86.8%
Common 33737
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 22546
 
10.1%
n 20053
 
9.0%
r 18714
 
8.4%
i 17500
 
7.9%
s 16281
 
7.3%
a 14990
 
6.7%
e 14914
 
6.7%
t 11232
 
5.0%
d 9994
 
4.5%
c 9928
 
4.5%
Other values (14) 66391
29.8%
Common
ValueCountFrequency (%)
33737
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33737
13.2%
o 22546
 
8.8%
n 20053
 
7.8%
r 18714
 
7.3%
i 17500
 
6.8%
s 16281
 
6.4%
a 14990
 
5.8%
e 14914
 
5.8%
t 11232
 
4.4%
d 9994
 
3.9%
Other values (15) 76319
29.8%

Cancelled Rides by Driver
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing131790
Missing (%)82.1%
Memory size9.7 MiB
1.0
28819 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters86457
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 28819
 
17.9%
(Missing) 131790
82.1%

Length

2026-01-29T11:21:42.309269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:42.361040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 28819
100.0%

Most occurring characters

ValueCountFrequency (%)
1 28819
33.3%
. 28819
33.3%
0 28819
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57638
66.7%
Other Punctuation 28819
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28819
50.0%
0 28819
50.0%
Other Punctuation
ValueCountFrequency (%)
. 28819
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 86457
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28819
33.3%
. 28819
33.3%
0 28819
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28819
33.3%
. 28819
33.3%
0 28819
33.3%

Driver Cancellation Reason
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing131790
Missing (%)82.1%
Memory size10.4 MiB
Customer related issue
7282 
The customer was coughing/sick
7211 
Personal & Car related issues
7176 
More than permitted people in there
7150 

Length

Max length35
Median length30
Mean length28.970054
Min length22

Characters and Unicode

Total characters834888
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal & Car related issues
2nd rowCustomer related issue
3rd rowCustomer related issue
4th rowPersonal & Car related issues
5th rowMore than permitted people in there

Common Values

ValueCountFrequency (%)
Customer related issue 7282
 
4.5%
The customer was coughing/sick 7211
 
4.5%
Personal & Car related issues 7176
 
4.5%
More than permitted people in there 7150
 
4.5%
(Missing) 131790
82.1%

Length

2026-01-29T11:21:42.436127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:42.509443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
customer 14493
 
11.2%
related 14458
 
11.2%
issue 7282
 
5.6%
the 7211
 
5.6%
was 7211
 
5.6%
coughing/sick 7211
 
5.6%
personal 7176
 
5.5%
7176
 
5.5%
car 7176
 
5.5%
issues 7176
 
5.5%
Other values (6) 42900
33.1%

Most occurring characters

ValueCountFrequency (%)
e 122304
14.6%
100651
12.1%
s 72183
 
8.6%
r 64753
 
7.8%
t 57551
 
6.9%
i 43180
 
5.2%
o 43180
 
5.2%
a 43171
 
5.2%
u 36162
 
4.3%
l 28784
 
3.4%
Other values (15) 222969
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 683855
81.9%
Space Separator 100651
 
12.1%
Uppercase Letter 35995
 
4.3%
Other Punctuation 14387
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 122304
17.9%
s 72183
10.6%
r 64753
9.5%
t 57551
8.4%
i 43180
 
6.3%
o 43180
 
6.3%
a 43171
 
6.3%
u 36162
 
5.3%
l 28784
 
4.2%
h 28722
 
4.2%
Other values (8) 143865
21.0%
Uppercase Letter
ValueCountFrequency (%)
C 14458
40.2%
T 7211
20.0%
P 7176
19.9%
M 7150
19.9%
Other Punctuation
ValueCountFrequency (%)
/ 7211
50.1%
& 7176
49.9%
Space Separator
ValueCountFrequency (%)
100651
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 719850
86.2%
Common 115038
 
13.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 122304
17.0%
s 72183
10.0%
r 64753
 
9.0%
t 57551
 
8.0%
i 43180
 
6.0%
o 43180
 
6.0%
a 43171
 
6.0%
u 36162
 
5.0%
l 28784
 
4.0%
h 28722
 
4.0%
Other values (12) 179860
25.0%
Common
ValueCountFrequency (%)
100651
87.5%
/ 7211
 
6.3%
& 7176
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 834888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 122304
14.6%
100651
12.1%
s 72183
 
8.6%
r 64753
 
7.8%
t 57551
 
6.9%
i 43180
 
5.2%
o 43180
 
5.2%
a 43171
 
5.2%
u 36162
 
4.3%
l 28784
 
3.4%
Other values (15) 222969
26.7%

Incomplete Rides
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing150946
Missing (%)94.0%
Memory size9.8 MiB
1.0
9663 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters28989
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 9663
 
6.0%
(Missing) 150946
94.0%

Length

2026-01-29T11:21:42.611457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:42.658875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 9663
100.0%

Most occurring characters

ValueCountFrequency (%)
1 9663
33.3%
. 9663
33.3%
0 9663
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19326
66.7%
Other Punctuation 9663
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9663
50.0%
0 9663
50.0%
Other Punctuation
ValueCountFrequency (%)
. 9663
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9663
33.3%
. 9663
33.3%
0 9663
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9663
33.3%
. 9663
33.3%
0 9663
33.3%

Incomplete Rides Reason
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing150946
Missing (%)94.0%
Memory size9.9 MiB
Customer Demand
3268 
Vehicle Breakdown
3236 
Other Issue
3159 

Length

Max length17
Median length15
Mean length14.362103
Min length11

Characters and Unicode

Total characters138781
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVehicle Breakdown
2nd rowOther Issue
3rd rowVehicle Breakdown
4th rowOther Issue
5th rowVehicle Breakdown

Common Values

ValueCountFrequency (%)
Customer Demand 3268
 
2.0%
Vehicle Breakdown 3236
 
2.0%
Other Issue 3159
 
2.0%
(Missing) 150946
94.0%

Length

2026-01-29T11:21:42.735185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:42.817969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
customer 3268
16.9%
demand 3268
16.9%
vehicle 3236
16.7%
breakdown 3236
16.7%
other 3159
16.3%
issue 3159
16.3%

Most occurring characters

ValueCountFrequency (%)
e 22562
16.3%
r 9663
 
7.0%
9663
 
7.0%
s 9586
 
6.9%
m 6536
 
4.7%
n 6504
 
4.7%
d 6504
 
4.7%
o 6504
 
4.7%
a 6504
 
4.7%
t 6427
 
4.6%
Other values (13) 48328
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 109792
79.1%
Uppercase Letter 19326
 
13.9%
Space Separator 9663
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22562
20.5%
r 9663
8.8%
s 9586
8.7%
m 6536
 
6.0%
n 6504
 
5.9%
d 6504
 
5.9%
o 6504
 
5.9%
a 6504
 
5.9%
t 6427
 
5.9%
u 6427
 
5.9%
Other values (6) 22575
20.6%
Uppercase Letter
ValueCountFrequency (%)
C 3268
16.9%
D 3268
16.9%
V 3236
16.7%
B 3236
16.7%
O 3159
16.3%
I 3159
16.3%
Space Separator
ValueCountFrequency (%)
9663
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 129118
93.0%
Common 9663
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22562
17.5%
r 9663
 
7.5%
s 9586
 
7.4%
m 6536
 
5.1%
n 6504
 
5.0%
d 6504
 
5.0%
o 6504
 
5.0%
a 6504
 
5.0%
t 6427
 
5.0%
u 6427
 
5.0%
Other values (12) 41901
32.5%
Common
ValueCountFrequency (%)
9663
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 22562
16.3%
r 9663
 
7.0%
9663
 
7.0%
s 9586
 
6.9%
m 6536
 
4.7%
n 6504
 
4.7%
d 6504
 
4.7%
o 6504
 
4.7%
a 6504
 
4.7%
t 6427
 
4.6%
Other values (13) 48328
34.8%

Driver Ratings
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)< 0.1%
Missing60943
Missing (%)37.9%
Infinite0
Infinite (%)0.0%
Mean4.2306825
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-29T11:21:42.902172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.3
Q14.1
median4.3
Q34.6
95-th percentile4.9
Maximum5
Range2
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.43697093
Coefficient of variation (CV)0.10328616
Kurtosis0.2766847
Mean4.2306825
Median Absolute Deviation (MAD)0.2
Skewness-0.6553693
Sum421655.2
Variance0.19094359
MonotonicityNot monotonic
2026-01-29T11:21:43.001645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4.3 15096
 
9.4%
4.2 14845
 
9.2%
4.6 10009
 
6.2%
4.4 7511
 
4.7%
4.1 7453
 
4.6%
4.7 5032
 
3.1%
4.9 5024
 
3.1%
4.5 4989
 
3.1%
3.9 4210
 
2.6%
3.8 4123
 
2.6%
Other values (11) 21374
 
13.3%
(Missing) 60943
37.9%
ValueCountFrequency (%)
3 798
 
0.5%
3.1 1558
 
1.0%
3.2 1652
 
1.0%
3.3 1573
 
1.0%
3.4 1612
 
1.0%
3.5 804
 
0.5%
3.6 2185
1.4%
3.7 4055
2.5%
3.8 4123
2.6%
3.9 4210
2.6%
ValueCountFrequency (%)
5 2532
 
1.6%
4.9 5024
 
3.1%
4.8 2495
 
1.6%
4.7 5032
 
3.1%
4.6 10009
6.2%
4.5 4989
 
3.1%
4.4 7511
4.7%
4.3 15096
9.4%
4.2 14845
9.2%
4.1 7453
4.6%

Customer Rating
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)< 0.1%
Missing60943
Missing (%)37.9%
Infinite0
Infinite (%)0.0%
Mean4.404499
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-29T11:21:43.093272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.6
Q14.2
median4.5
Q34.8
95-th percentile5
Maximum5
Range2
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.43777579
Coefficient of variation (CV)0.099392869
Kurtosis0.64585631
Mean4.404499
Median Absolute Deviation (MAD)0.3
Skewness-0.88417172
Sum438978.8
Variance0.19164765
MonotonicityNot monotonic
2026-01-29T11:21:43.514741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4.9 12463
 
7.8%
4.6 12395
 
7.7%
4.3 11812
 
7.4%
4.2 11468
 
7.1%
4.8 6306
 
3.9%
4.5 6278
 
3.9%
5 6267
 
3.9%
4.7 6149
 
3.8%
4.1 5802
 
3.6%
4.4 5628
 
3.5%
Other values (11) 15098
 
9.4%
(Missing) 60943
37.9%
ValueCountFrequency (%)
3 500
 
0.3%
3.1 1083
0.7%
3.2 938
 
0.6%
3.3 961
 
0.6%
3.4 1003
 
0.6%
3.5 474
 
0.3%
3.6 1263
0.8%
3.7 2533
1.6%
3.8 2541
1.6%
3.9 2527
1.6%
ValueCountFrequency (%)
5 6267
3.9%
4.9 12463
7.8%
4.8 6306
3.9%
4.7 6149
3.8%
4.6 12395
7.7%
4.5 6278
3.9%
4.4 5628
3.5%
4.3 11812
7.4%
4.2 11468
7.1%
4.1 5802
3.6%

Payment Method
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing51280
Missing (%)31.9%
Memory size9.7 MiB
UPI
49223 
Cash
27176 
Uber Wallet
13151 
Credit Card
10959 
Debit Card
8820 

Length

Max length11
Median length10
Mean length5.5775046
Min length3

Characters and Unicode

Total characters609783
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUPI
2nd rowDebit Card
3rd rowUPI
4th rowUPI
5th rowUPI

Common Values

ValueCountFrequency (%)
UPI 49223
30.6%
Cash 27176
16.9%
Uber Wallet 13151
 
8.2%
Credit Card 10959
 
6.8%
Debit Card 8820
 
5.5%
(Missing) 51280
31.9%

Length

2026-01-29T11:21:43.635580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-29T11:21:43.715557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
upi 49223
34.6%
cash 27176
19.1%
card 19779
13.9%
uber 13151
 
9.2%
wallet 13151
 
9.2%
credit 10959
 
7.7%
debit 8820
 
6.2%

Most occurring characters

ValueCountFrequency (%)
U 62374
10.2%
a 60106
9.9%
C 57914
9.5%
P 49223
 
8.1%
I 49223
 
8.1%
e 46081
 
7.6%
r 43889
 
7.2%
32930
 
5.4%
t 32930
 
5.4%
d 30738
 
5.0%
Other values (7) 144375
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 336148
55.1%
Uppercase Letter 240705
39.5%
Space Separator 32930
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 60106
17.9%
e 46081
13.7%
r 43889
13.1%
t 32930
9.8%
d 30738
9.1%
h 27176
8.1%
s 27176
8.1%
l 26302
7.8%
b 21971
 
6.5%
i 19779
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
U 62374
25.9%
C 57914
24.1%
P 49223
20.4%
I 49223
20.4%
W 13151
 
5.5%
D 8820
 
3.7%
Space Separator
ValueCountFrequency (%)
32930
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 576853
94.6%
Common 32930
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 62374
10.8%
a 60106
10.4%
C 57914
10.0%
P 49223
8.5%
I 49223
8.5%
e 46081
 
8.0%
r 43889
 
7.6%
t 32930
 
5.7%
d 30738
 
5.3%
h 27176
 
4.7%
Other values (6) 117199
20.3%
Common
ValueCountFrequency (%)
32930
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 609783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 62374
10.2%
a 60106
9.9%
C 57914
9.5%
P 49223
 
8.1%
I 49223
 
8.1%
e 46081
 
7.6%
r 43889
 
7.2%
32930
 
5.4%
t 32930
 
5.4%
d 30738
 
5.0%
Other values (7) 144375
23.7%

Interactions

2026-01-29T11:21:30.737661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:21.690877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:22.820177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:23.908386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:24.990854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:25.966612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:27.238972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:28.365106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:29.404122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:30.882987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:21.852311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:22.921412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:24.025929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:25.099910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:26.090178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:27.399281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:28.470941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:29.536304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:31.013834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:21.986414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:23.042396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:24.143870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:25.208953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:26.417592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:27.534400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:28.591925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:29.644106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:31.147504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:22.101729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:23.139725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:24.261317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:25.313849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:26.524037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:27.643849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:28.684675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:29.755125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:31.299404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:22.199635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:23.270241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:24.371080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:25.420422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:26.636427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:27.755436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:28.795143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:29.902550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:31.438096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:22.326033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:23.394128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:24.502975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:25.516184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:26.766349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:27.874722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:28.925761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:30.028757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:31.624045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:22.445491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:23.514185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:24.614590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:25.629969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:26.886336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:27.988750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:29.036072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:30.168401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:31.792078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:22.561163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:23.634592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:24.724761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:25.740706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:26.986466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:28.106348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:29.155949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:30.398875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:31.925042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:22.685068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:23.783195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:24.858072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:25.858632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:27.124990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:28.224882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:29.285179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-29T11:21:30.571383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-29T11:21:43.818174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Avg CTATAvg VTATBooking StatusBooking ValueCustomer RatingDriver Cancellation ReasonDriver RatingsEventIncomplete Rides ReasonPayment MethodReason for cancelling by CustomerRide DistanceVehicle Typedayhourmonthweekday
Avg CTAT1.0000.0580.4710.0000.0010.0000.0010.0040.0000.0000.0000.1010.0010.000-0.001-0.0010.005
Avg VTAT0.0581.0000.3380.005-0.0030.000-0.0050.0040.0000.0000.0140.0630.0000.004-0.0050.0030.003
Booking Status0.4710.3381.0000.0061.0001.0001.0000.0001.0000.0001.0000.3530.0030.0000.0000.0010.004
Booking Value0.0000.0050.0061.000-0.0040.0000.0010.0170.0000.0000.0000.0040.0000.0080.0010.0000.108
Customer Rating0.001-0.0031.000-0.0041.0000.000-0.0020.0060.0000.0050.0000.0030.005-0.001-0.003-0.0000.000
Driver Cancellation Reason0.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0100.0090.0090.007
Driver Ratings0.001-0.0051.0000.001-0.0020.0001.0000.0000.0000.0000.000-0.0020.000-0.0000.001-0.0010.006
Event0.0040.0040.0000.0170.0060.0000.0001.0000.0180.0000.0150.0040.0000.2330.0000.4770.156
Incomplete Rides Reason0.0000.0001.0000.0000.0000.0000.0000.0181.0000.0000.0000.0100.0160.0200.0060.0000.014
Payment Method0.0000.0000.0000.0000.0050.0000.0000.0000.0001.0000.0000.0050.0060.0060.0030.0060.004
Reason for cancelling by Customer0.0000.0141.0000.0000.0000.0000.0000.0150.0000.0001.0000.0000.1680.0000.0000.0120.000
Ride Distance0.1010.0630.3530.0040.0030.000-0.0020.0040.0100.0050.0001.0000.0000.006-0.0020.0050.000
Vehicle Type0.0010.0000.0030.0000.0050.0000.0000.0000.0160.0060.1680.0001.0000.0030.0040.0030.003
day0.0000.0040.0000.008-0.0010.010-0.0000.2330.0200.0060.0000.0060.0031.000-0.003-0.0190.069
hour-0.001-0.0050.0000.001-0.0030.0090.0010.0000.0060.0030.000-0.0020.004-0.0031.0000.0000.000
month-0.0010.0030.0010.000-0.0000.009-0.0010.4770.0000.0060.0120.0050.003-0.0190.0001.0000.044
weekday0.0050.0030.0040.1080.0000.0070.0060.1560.0140.0040.0000.0000.0030.0690.0000.0441.000

Missing values

2026-01-29T11:21:32.304157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-29T11:21:32.921153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-29T11:21:34.349566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datetimeDateTimeyearmonthdayweekdayhourride_dateEventBooking IDBooking StatusCustomer IDVehicle TypePickup LocationDrop LocationRide DistanceBooking ValueAvg VTATAvg CTATCancelled Rides by CustomerReason for cancelling by CustomerCancelled Rides by DriverDriver Cancellation ReasonIncomplete RidesIncomplete Rides ReasonDriver RatingsCustomer RatingPayment Method
02024-03-23 12:29:382024-03-2312:29:382024323Saturday122024-03-23Normal Day"CNR5884300"No Driver Found"CID1982111"eBikePalam ViharJhilmilNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12024-11-29 18:01:392024-11-2918:01:3920241129Friday182024-11-29Wedding_Season"CNR1326809"Incomplete"CID4604802"Go SedanShastri NagarGurgaon Sector 565.73237.04.914.0NaNNaNNaNNaN1.0Vehicle BreakdownNaNNaNUPI
22024-08-23 08:56:102024-08-2308:56:102024823Friday82024-08-23Monsoon"CNR8494506"Completed"CID9202816"AutoKhandsaMalviya Nagar13.58627.013.425.8NaNNaNNaNNaNNaNNaN4.94.9Debit Card
32024-10-21 17:17:252024-10-2117:17:2520241021Monday172024-10-21Normal Day"CNR8906825"Completed"CID2610914"Premier SedanCentral SecretariatInderlok34.02416.013.128.5NaNNaNNaNNaNNaNNaN4.65.0UPI
42024-09-16 22:08:002024-09-1622:08:002024916Monday222024-09-16Normal Day"CNR1950162"Completed"CID9933542"BikeGhitorni VillageKhan Market48.21737.05.319.6NaNNaNNaNNaNNaNNaN4.14.3UPI
52024-02-06 09:44:562024-02-0609:44:56202426Tuesday92024-02-06Wedding_Season"CNR4096693"Completed"CID4670564"AutoAIIMSNarsinghpur4.85316.05.118.1NaNNaNNaNNaNNaNNaN4.14.6UPI
62024-06-17 15:45:582024-06-1715:45:582024617Monday152024-06-17Eid-ul-Adha"CNR2002539"Completed"CID6800553"Go MiniVaishaliPunjabi Bagh41.24640.07.120.4NaNNaNNaNNaNNaNNaN4.04.1UPI
72024-03-19 17:37:372024-03-1917:37:372024319Tuesday172024-03-19Normal Day"CNR6568000"Completed"CID8610436"AutoMayur ViharCyber Hub6.56136.012.116.5NaNNaNNaNNaNNaNNaN4.44.2UPI
82024-09-14 12:49:092024-09-1412:49:092024914Saturday122024-09-14Monsoon"CNR4510807"No Driver Found"CID7873618"Go SedanNoida Sector 62Noida Sector 18NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
92024-12-16 19:06:482024-12-1619:06:4820241216Monday192024-12-16Normal Day"CNR7721892"Incomplete"CID5214275"AutoRohiniAdarsh Nagar10.36135.06.126.0NaNNaNNaNNaN1.0Other IssueNaNNaNCash
datetimeDateTimeyearmonthdayweekdayhourride_dateEventBooking IDBooking StatusCustomer IDVehicle TypePickup LocationDrop LocationRide DistanceBooking ValueAvg VTATAvg CTATCancelled Rides by CustomerReason for cancelling by CustomerCancelled Rides by DriverDriver Cancellation ReasonIncomplete RidesIncomplete Rides ReasonDriver RatingsCustomer RatingPayment Method
1605992024-07-13 14:47:302024-07-1314:47:302024713Saturday142024-07-13Monsoon"CNR5591053"Completed"CID1829616"AutoKarol BaghVishwavidyalaya27.91597.011.230.8NaNNaNNaNNaNNaNNaN4.24.3UPI
1606002024-01-24 18:02:282024-01-2418:02:282024124Wednesday182024-01-24Normal Day"CNR1717894"No Driver Found"CID9564749"Go MiniNehru PlaceAya NagarNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1606012024-05-03 11:18:172024-05-0311:18:17202453Friday112024-05-03Normal Day"CNR9715958"Completed"CID8835432"Go MiniTughlakabadJama Masjid29.89280.013.220.1NaNNaNNaNNaNNaNNaN4.14.7Cash
1606022024-06-14 16:46:532024-06-1416:46:532024614Friday162024-06-14Normal Day"CNR9572383"Completed"CID2952237"Go MiniAkshardhamGreater Noida10.51388.08.230.6NaNNaNNaNNaNNaNNaN4.34.9UPI
1606032024-11-11 19:34:012024-11-1119:34:0120241111Monday192024-11-11Wedding_Season"CNR6500631"Completed"CID4337371"Go MiniMG RoadGhitorni40.08475.010.244.4NaNNaNNaNNaNNaNNaN3.74.1Uber Wallet
1606042024-11-24 15:55:092024-11-2415:55:0920241124Sunday152024-11-24Wedding_Season"CNR2468611"Completed"CID2325623"Go MiniGolf Course RoadAkshardham21.311093.05.130.8NaNNaNNaNNaNNaNNaN4.85.0UPI
1606052024-09-18 10:55:152024-09-1810:55:152024918Wednesday102024-09-18Normal Day"CNR6358306"Completed"CID9925486"Go SedanSatguru Ram Singh MargJor Bagh15.93852.02.723.4NaNNaNNaNNaNNaNNaN3.94.4Cash
1606062024-10-05 07:53:342024-10-0507:53:342024105Saturday72024-10-05Navratri"CNR3030099"Completed"CID9415487"AutoGhaziabadSaidulajab45.54333.06.939.6NaNNaNNaNNaNNaNNaN4.13.7UPI
1606072024-10-05 07:53:342024-10-0507:53:342024105Saturday72024-10-05Festive_Sales"CNR3030099"Completed"CID9415487"AutoGhaziabadSaidulajab45.54333.06.939.6NaNNaNNaNNaNNaNNaN4.13.7UPI
1606082024-03-10 15:38:032024-03-1015:38:032024310Sunday152024-03-10Wedding_Season"CNR3447390"Completed"CID4108667"Premier SedanAshok Park MainGurgaon Sector 2921.19806.03.533.7NaNNaNNaNNaNNaNNaN4.64.9Credit Card

Duplicate rows

Most frequently occurring

datetimeDateTimeyearmonthdayweekdayhourride_dateEventBooking IDBooking StatusCustomer IDVehicle TypePickup LocationDrop LocationRide DistanceBooking ValueAvg VTATAvg CTATCancelled Rides by CustomerReason for cancelling by CustomerCancelled Rides by DriverDriver Cancellation ReasonIncomplete RidesIncomplete Rides ReasonDriver RatingsCustomer RatingPayment Method# duplicates
302024-01-13 09:16:532024-01-1309:16:532024113Saturday92024-01-13Normal Day"CNR1665871"Cancelled by Customer"CID4830996"BikeLal QuilaVasant KunjNaNNaN15.3NaN1.0Change of plansNaNNaNNaNNaNNaNNaNNaN3
312024-01-13 09:16:532024-01-1309:16:532024113Saturday92024-01-13Normal Day"CNR3467484"Cancelled by Customer"CID1585469"Uber XLSadar Bazar GurgaonAdarsh NagarNaNNaN18.7NaN1.0Change of plansNaNNaNNaNNaNNaNNaNNaN3
322024-01-13 09:16:532024-01-1309:16:532024113Saturday92024-01-13Normal Day"CNR5995469"Completed"CID1368457"eBikeMalviya NagarHero Honda Chowk7.66445.05.822.4NaNNaNNaNNaNNaNNaN4.64.7Credit Card3
02024-01-01 21:54:162024-01-0121:54:16202411Monday212024-01-01New Year"CNR9901275"Completed"CID9869633"Go SedanVatika ChowkInderlok4.61202.02.340.1NaNNaNNaNNaNNaNNaN4.74.2Cash2
12024-01-01 21:54:162024-01-0121:54:16202411Monday212024-01-01New Year"CNR9918929"Completed"CID7591176"Premier SedanPalam ViharShastri Park14.89349.07.130.7NaNNaNNaNNaNNaNNaN4.14.0UPI2
22024-01-02 17:16:292024-01-0217:16:29202412Tuesday172024-01-02Normal Day"CNR5455862"Cancelled by Driver"CID3909649"Go SedanAkshardhamMalviya NagarNaNNaN7.6NaNNaNNaN1.0Personal & Car related issuesNaNNaNNaNNaNNaN2
32024-01-02 17:16:292024-01-0217:16:29202412Tuesday172024-01-02Normal Day"CNR9650422"Completed"CID4731274"Go MiniOld GurgaonMoti Nagar34.061917.010.119.0NaNNaNNaNNaNNaNNaN4.64.9UPI2
42024-01-02 17:57:472024-01-0217:57:47202412Tuesday172024-01-02Normal Day"CNR8346445"Completed"CID9173008"BikePitampuraVishwavidyalaya46.67160.02.117.6NaNNaNNaNNaNNaNNaN4.24.2UPI2
52024-01-02 17:57:472024-01-0217:57:47202412Tuesday172024-01-02Normal Day"CNR8949149"Completed"CID8694169"Go SedanBarakhamba RoadDwarka Mor42.66919.010.226.3NaNNaNNaNNaNNaNNaN4.24.1UPI2
62024-01-03 09:36:432024-01-0309:36:43202413Wednesday92024-01-03Normal Day"CNR5832024"Cancelled by Driver"CID8105586"AutoITOINA MarketNaNNaN7.2NaNNaNNaN1.0More than permitted people in thereNaNNaNNaNNaNNaN2